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Crate ndarray

Expand description

The ndarray crate provides an n-dimensional container for general elements and for numerics.

In n-dimensional we include, for example, 1-dimensional rows or columns, 2-dimensional matrices, and higher dimensional arrays. If the array has n dimensions, then an element in the array is accessed by using that many indices. Each dimension is also called an axis.

To get started, functionality is provided in the following core types:

ArrayBase: The n-dimensional array type itself.
It is used to implement both the owned arrays and the views; see its docs for an overview of all array features.
The main specific array type is Array, which owns its elements.
A reference type, ArrayRef, that contains most of the functionality for reading and writing to arrays.
A reference type, LayoutRef, that contains most of the functionality for reading and writing to array layouts: their shape and strides.

§Highlights

Generic n-dimensional array
Slicing, also with arbitrary step size, and negative indices to mean elements from the end of the axis.
Views and subviews of arrays; iterators that yield subviews.
Higher order operations and arithmetic are performant
Array views can be used to slice and mutate any [T] data using ArrayView::from and ArrayViewMut::from.
Zip for lock step function application across two or more arrays or other item producers (NdProducer trait).

§Crate Status

Still iterating on and evolving the crate
- The crate is continuously developing, and breaking changes are expected during evolution from version to version. We adopt the newest stable rust features if we need them.
- Note that functions/methods/traits/etc. hidden from the docs are not considered part of the public API, so changes to them are not considered breaking changes.
Performance:
- Prefer higher order methods and arithmetic operations on arrays first, then iteration, and as a last priority using indexed algorithms.
- The higher order functions like .map(), .map_inplace(), .zip_mut_with(), Zip and azip!() are the most efficient ways to perform single traversal and lock step traversal respectively.
- Performance of an operation depends on the memory layout of the array or array view. Especially if it’s a binary operation, which needs matching memory layout to be efficient (with some exceptions).
- Efficient floating point matrix multiplication even for very large matrices; can optionally use BLAS to improve it further.
MSRV: Requires Rust 1.64 or later

§Crate Feature Flags

The following crate feature flags are available. They are configured in your Cargo.toml. See doc::crate_feature_flags for more information.

std: Rust standard library-using functionality (enabled by default)
serde: serialization support for serde 1.x
rayon: Parallel iterators, parallelized methods, the parallel module and par_azip!.
approx Implementations of traits from the approx crate.
blas: transparent BLAS support for matrix multiplication, needs configuration.
matrixmultiply-threading: Use threading from matrixmultiply.

§Documentation

The docs for ArrayBase provide an overview of the n-dimensional array type. Other good pages to look at are the documentation for the s![] and azip!() macros.
If you have experience with NumPy, you may also be interested in ndarray_for_numpy_users.

§The ndarray ecosystem

ndarray provides a lot of functionality, but it’s not a one-stop solution.

ndarray includes matrix multiplication and other binary/unary operations out of the box. More advanced linear algebra routines (e.g. SVD decomposition or eigenvalue computation) can be found in ndarray-linalg.

The same holds for statistics: ndarray provides some basic functionalities (e.g. mean) but more advanced routines can be found in ndarray-stats.

If you are looking to generate random arrays instead, check out ndarray-rand.

For conversion between ndarray, nalgebra and image check out nshare.

Re-exports§

pub use crate::slice::MultiSliceArg;
pub use crate::slice::NewAxis;
pub use crate::slice::Slice;
pub use crate::slice::SliceArg;
pub use crate::slice::SliceInfo;
pub use crate::slice::SliceInfoElem;

Modules§

docdocsrs: Standalone documentation pages.
iter: Producers, iterables and iterators.
linalg: Linear algebra.
parallelrayon: Parallelization features for ndarray.
prelude: ndarray prelude.

Macros§

array: Create an Array with one, two, three, four, five, or six dimensions.
azip: Array zip macro: lock step function application across several arrays and producers.
concatenate: Concatenate arrays along the given axis.
par_aziprayon: Parallelized array zip macro: lock step function application across several arrays and producers.
s: Slice argument constructor.
stack: Stack arrays along the new axis.

Structs§

ArrayBase: An n-dimensional array.
ArrayRef: A reference to an n-dimensional array whose data is safe to read and write.
Axis: An axis index.
AxisDescription: Description of the axis, its length and its stride.
Dim: Dimension description.
IxDynImpl: Dynamic dimension or index type.
LayoutRef: A reference to the layout of an n-dimensional array.
Linspacestd: An iterator of a sequence of evenly spaced floats.
Logspacestd: An iterator of a sequence of logarithmically spaced number.
MathCell: A transparent wrapper of Cell<T> which is identical in every way, except it will implement arithmetic operators as well.
NewAxis: Token to represent a new axis in a slice description.
OwnedArcRepr: ArcArray’s representation.
OwnedRepr: Array’s representation.
RawRef: A reference to an n-dimensional array whose data is not safe to read or write.
RawViewRepr: Array pointer’s representation.
Shape: A contiguous array shape of n dimensions.
ShapeError: An error related to array shape or layout.
Slice: A slice (range with step size).
SliceInfo: Represents all of the necessary information to perform a slice.
StrideShape: An array shape of n dimensions in c-order, f-order or custom strides.
ViewRepr: Array view’s representation.
Zip: Lock step function application across several arrays or other producers.

Enums§

CowRepr: CowArray’s representation.
ErrorKind: Error code for an error related to array shape or layout.
FoldWhile: Value controlling the execution of .fold_while on Zip.
MeshIndex: The indexing order for meshgrid; see there for more details.
Order: Array order
SliceInfoElem: A slice (range with step), an index, or a new axis token.

Traits§

AsArray: Argument conversion into an array view
AssignElem: A producer element that can be assigned to once
Data: Array representation trait.
DataMut: Array representation trait.
DataOwned: Array representation trait.
DataShared: Array representation trait.
DimAdd: Adds the two dimensions at compile time.
DimMax: A trait to specify when one dimension is strictly larger than another.
Dimension: Array shape and index trait.
IndexLonger: Extra indexing methods for array views
IntoDimension: Argument conversion a dimension.
IntoNdProducer: Argument conversion into a producer.
LinalgScalar: Elements that support linear algebra operations.
MultiSliceArg: Slicing information describing multiple mutable, disjoint slices.
NdFloatstd: Floating-point element types f32 and f64.
NdIndex: Tuple or fixed size arrays that can be used to index an array.
NdProducer: A producer of an n-dimensional set of elements; for example an array view, mutable array view or an iterator that yields chunks.
RawData: Array representation trait.
RawDataClone: Array representation trait.
RawDataMut: Array representation trait.
RawDataSubst: Array representation trait.
RemoveAxis: Array shape with a next smaller dimension.
ScalarOperand: Elements that can be used as direct operands in arithmetic with arrays.
ShapeArg: Array shape argument with optional order parameter
ShapeBuilder: A trait for Shape and D where D: Dimension that allows customizing the memory layout (strides) of an array shape.
SliceArg: A type that can slice an array of dimension D.

Functions§

Dim: Create a new dimension value.
Ix0: Create a zero-dimensional index
Ix1: Create a one-dimensional index
Ix2: Create a two-dimensional index
Ix3: Create a three-dimensional index
Ix4: Create a four-dimensional index
Ix5: Create a five-dimensional index
Ix6: Create a six-dimensional index
IxDyn: Create a dynamic-dimensional index
arr0: Create a zero-dimensional array with the element x.
arr1: Create a one-dimensional array with elements from xs.
arr2: Create a two-dimensional array with elements from xs.
arr3: Create a three-dimensional array with elements from xs.
aview0: Create a zero-dimensional array view borrowing x.
aview1: Create a one-dimensional array view with elements borrowing xs.
aview2: Create a two-dimensional array view with elements borrowing xs.
aview_mut1: Create a one-dimensional read-write array view with elements borrowing xs.
aview_mut2: Create a two-dimensional read-write array view with elements borrowing xs.
concatenate: Concatenate arrays along the given axis.
indices: Create an iterable of the array shape shape.
indices_of: Return an iterable of the indices of the passed-in array.
linspacestd: Return an iterator of evenly spaced floats.
logspacestd: An iterator of a sequence of logarithmically spaced numbers.
meshgrid: Create coordinate matrices from coordinate vectors.
rangestd: Return an iterator of floats from a to b (exclusive), incrementing by step.
rcarr1: Create a one-dimensional array with elements from xs.
rcarr2: Create a two-dimensional array with elements from xs.
rcarr3: Create a three-dimensional array with elements from xs.
stack: Stack arrays along the new axis.

Type Aliases§

ArcArray: An array where the data has shared ownership and is copy on write.
ArcArray1: one-dimensional shared ownership array
ArcArray2: two-dimensional shared ownership array
ArcArray3: three-dimensional shared ownership array
ArcArray4: four-dimensional shared ownership array
ArcArray5: five-dimensional shared ownership array
ArcArray6: six-dimensional shared ownership array
ArcArrayD: dynamic-dimensional shared ownership array
Array: An array that owns its data uniquely.
Array0: zero-dimensional array
Array1: one-dimensional array
Array2: two-dimensional array
Array3: three-dimensional array
Array4: four-dimensional array
Array5: five-dimensional array
Array6: six-dimensional array
ArrayD: dynamic-dimensional array
ArrayRef0: zero-dimensional array reference
ArrayRef1: one-dimensional array reference
ArrayRef2: two-dimensional array reference
ArrayRef3: three-dimensional array reference
ArrayRef4: four-dimensional array reference
ArrayRef5: five-dimensional array reference
ArrayRef6: six-dimensional array reference
ArrayRefD: dynamic-dimensional array reference
ArrayView: A read-only array view.
ArrayView0: zero-dimensional array view
ArrayView1: one-dimensional array view
ArrayView2: two-dimensional array view
ArrayView3: three-dimensional array view
ArrayView4: four-dimensional array view
ArrayView5: five-dimensional array view
ArrayView6: six-dimensional array view
ArrayViewD: dynamic-dimensional array view
ArrayViewMut: A read-write array view.
ArrayViewMut0: zero-dimensional read-write array view
ArrayViewMut1: one-dimensional read-write array view
ArrayViewMut2: two-dimensional read-write array view
ArrayViewMut3: three-dimensional read-write array view
ArrayViewMut4: four-dimensional read-write array view
ArrayViewMut5: five-dimensional read-write array view
ArrayViewMut6: six-dimensional read-write array view
ArrayViewMutD: dynamic-dimensional read-write array view
CowArray: An array with copy-on-write behavior.
Ix: Array index type
Ix0: zero-dimensionial
Ix1: one-dimensional
Ix2: two-dimensional
Ix3: three-dimensional
Ix4: four-dimensional
Ix5: five-dimensional
Ix6: six-dimensional
IxDyn: dynamic-dimensional
Ixs: Array index type (signed)
LayoutRef0: zero-dimensional layout reference
LayoutRef1: one-dimensional layout reference
LayoutRef2: two-dimensional layout reference
LayoutRef3: three-dimensional layout reference
LayoutRef4: four-dimensional layout reference
LayoutRef5: five-dimensional layout reference
LayoutRef6: six-dimensional layout reference
LayoutRefD: dynamic-dimensional layout reference
RawArrayView: A read-only array view without a lifetime.
RawArrayViewMut: A mutable array view without a lifetime.

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